Background and Purpose: Unmanned aerial vehicles (UAVs) are flexible to solve various surveying tasks which make them useful in many disciplines, including forestry. The main goal of this research is to evaluate the quality of photogrammetry-based digital surface model (DSM) from low-cost UAV’s images collected in non-optimal weather (windy and cloudy weather) and environmental (inaccessibility for regular spatial distribution of ground control points - GCPs) conditions. Materials and Methods: The UAV-based DSMs without (DSMP) and with using GCPs (DSMP-GCP) were generated. The vertical agreement assessment of the UAV-based DSMs was conducted by comparing elevations of 60 checkpoints of a regular 100 m sampling grid obtained from LiDAR-based DSM (DSML) with the elevations of planimetrically corresponding points obtained from DSMP and DSMP-GCP. Due to the non-normal distribution of residuals (vertical differences between UAV- and LiDAR-based DSMs), a vertical agreement was assessed by using robust measures: median, normalised median absolute deviation (NMAD), 68.3% quantile and 95% quantile. Results: As expected, DSMP-GCP shows higher accuracy, i.e. higher vertical agreement with DSML than DSMP. The median, NMAD, 68.3% quantile, 95% quantile and RMSE* (without outliers) values for DSMP are 2.23 m, 3.22 m, 4.34 m, 15.04 m and 5.10 m, respectively, whereas for DSMP-GCP amount to -1.33 m, 2.77 m, 0.11 m, 8.15 m and 3.54 m, respectively. Conclusions: The obtained results confirmed great potential of images obtained by low-cost UAV for forestry applications, even if they are surveyed in non-optimal weather and environmental conditions. This could be of importance for cases when urgent UAV surveys are needed (e.g. detection and estimation of forest damage) which do not allow careful and longer survey planning. The vertical agreement assessment of UAV-based DSMs with LiDAR-based DSM confirmed the importance of GCPs for image orientation and DSM generation. Namely, a considerable improvement in vertical accuracy of UAV-based DSMs was observed when GCPs were used.

Today we are witnessing the growing use of unmanned aerial vehicles (UAVs) for monitoring purposes. Potential applications of UAVs can be found in agricultural, forestry, and environmental sciences; surveillance, and reconnaissance; aerial monitoring in engineering; cultural heritage; and traditional surveying, conventional mapping and photogrammetry, and cadastral applications [1]. Due to various construction solutions UAVs are flexibile to solve various surveying tasks. Compared to the classical terrestrial survey, UAVs are capable to cover considerably larger areas in short time period, as well as to survey distant or inaccessible areas (e.g. distant forest and mined areas) and objects (e.g. high buildings). The flexibility of photogrammetric surveying methods along with the selection of the adequate cameras and lenses results in adaption of the measuring platform (UAV) to the needs of the tasks. Furthermore, UAVs have a capability of an autonomous recording, and hence they are becoming independent devices for gathering a large number of high-quality data of the field or object with appropriate accuracy.

Recently, comprehensive reviews on applications of UAVs in forestry have been provided by Tang and Shao [2] and Torresan et al. [3]. In general, the common UAVs applications in forestry are related to monitoring of forest health and disturbances [4-6], forest inventory [7, 8], forest cover mapping [9], etc. Digital surface model (DSM), which is one of the main photogrammetric products of UAV surveys, has great application in forest inventory. By subtracting available digital terrain model (DTM), which presents terrain surface, from DSM, which presents forest surface, a canopy height model (CHM) is generated. DTMs are nowadays commonly generated using airborne laser scanning (ALS) technology based on light detection and ranging (LiDAR) or airborne digital photogrammetry [10]. From CHMs various metrics can be derived which are then used for estimation of various tree [11] and stand variables [7, 12]. The Structure from Motion (SfM) algorithm has been suggested for DSM generation by many authors [13-15]. Camera calibration and image phototriangulation process are initially performed to generate accurate DSM or digital terrain model (DTM) [16]. Camera calibration method and the algorithm for the precise elimination of lens distortion on digital cameras was developed by Gašparović and Gajski [17]. Continuing the research Gašparović and Gajski [18] presented a new method of two-step camera calibration for micro UAVs.

The DSMs generated from airborne digital stereo images were evaluated in many studies [e.g. 23-25] which revealed that many factors may influence on their quality, especially in complex forest structure. The research on DSM quality obtained from UAV images are still lacking (especially in South-east European region), but it can be assumed that apart from technical characteristics related to UAV (e.g. camera quality, GNSS precision) similar factors (e.g. image quality, algorithm for image processing, weather conditions, forest structure, etc.) are present.

The main goal of this research is to evaluate the quality of photogrammetry-based DSM from low-cost UAV’s images collected in non-optimal weather (windy and cloudy weather) and environmental (inaccessibility for regular spatial distribution of GCPs) conditions. Namely, urgent cases (e.g. detection and estimation of forest damage) sometimes require rapid and immediate reaction when data acquisitions have to be done in non-optimal weather conditions during the survey. Furthermore, in dense forests it is very difficult to find a place for GCPs, especially to obtain the regular spatial distribution of GCPs which will provide the most accurate orientation of images. Therefore, vertical agreement assessment of UAV-based DSMs generated without and with using GCPs was evaluated with LiDAR-based DSM in this study.

The UAV images were acquired using the DJI Phantom 4 Pro UAV with FC6310 camera (Table 1) on 14 September 2017. The average ﬂying height was 200 m above ground level. The study area was covered by 488 RGB images with the ground sampling distance (GSD) of approximately 5 cm. The images were collected in 11 ﬂight lines with endlap of 90% and sidelap of 80%. Weather conditions during UAV survey were not suitable (non-optimal) due to windy and cloudy weather.

Before the UAV survey, seven ground control points (GCPs) were placed and measured in the study area (Figure 1). The GCPs’ positions (x, y, z coordinates) were measured using the Trimble GNSS receiver connected with the Croatian Positioning System (CROPOS) which enables to obtain both horizontal and vertical positional accuracy from 2 to 5 cm (CROPOS - Users’ Manual). Due to dense forest and mostly invisible ground from the air, it was not possible to provide (set up) the regular spatial distribution of GCPs over the entire study area which enables the most accurate orientation of images [26]. Therefore, GCPs were set up and measured on the forest roads from where they can be easily detected on UAV images (Figure 1).

From the collected UAV images, two DSMs were generated. First DSM was generated without using GCPs. This means that DSM was generated from UAV images whose orientation was based on a priori exterior orientation parameters (EOPs) only. A priori EOPs were measured during flight in metadata files of each image by GNSS. Firstly, tie-points on all images were automatically determined using the Structure from Motion (SfM) algorithm. Image coordinates of tie-points and a priori EOPs were then used for photo-triangulation with self-calibration. By automatic correlation of oriented images, the point cloud was obtained and then used to generate raster DSM (hereinafter referred to as DSMP) with a spatial resolution of 0.5 m.

To generate the second DSM, the classic image photo-triangulation method based on tie-points and GCPs was used. Tie-points on all images, as in the previous case, were automatically determined using SfM algorithm. Photo-triangulation with self-calibration was based on image coordinates of tie-points and GCPs, and GCPs’ coordinates in the terrestrial coordinate system. A priori EOPs were not used in this case. A raster DSM (hereinafter referred to as DSMP-GCP) with a spatial resolution of 0.5 m was generated from the point cloud obtained by automatic correlation of oriented images.

The whole procedure of image orientation and DSMs generation was performed using Agisoft PhotoScan software (version 1.2.6, 64 bit).

LiDAR-Based Canopy Digital Surface Model

A raster LiDAR-based DSM (hereinafter referred to as DSML) with a spatial resolution of 0.5 m was provided by Hrvatske vode Ltd. (Zagreb, Croatia). Table 2 provides an overview of LiDAR sensor and data characteristics used for DSML generation. The resulting point densities (11.59 points·m-2) and the stated horizontal (0.15 m) and vertical (0.08 m) accuracies were based on a considerably larger area (which included and non-forested areas as well) than the one considered in this study. DSML was generated from returns classified as “first return” and “only return”. DSML was used as reference data for vertical agreement assessment of UAV-based DSMs (DSMP and DSMP-GCP). Due to its high accuracy, the LiDAR data were often used as reference data for evaluation of UAV data [27-29].

The vertical agreement assessment of the UAV-based DSMs was conducted by comparing elevations of 60 checkpoints of a regular 100 m grid obtained from DSML with the elevations of planimetrically corresponding points obtained from DSMP and DSMP-GCP. Prior to defining measures for agreement assessment, the normality of residuals (vertical errors between UAV- and LiDAR-based DSMs) distribution was analyzed using: (a) histograms with a superimposed curve indicating normal distribution, (b) Shapiro-Wilk test [30, 31], and (c) normal Q-Q plots (Figure 2). All performed tests revealed non-normal distribution of vertical errors for both UAV-based DSMs. Consequently, the following robust measures suggested by Höhle and Höhle [10] were used for vertical agreement assessment: median, normalised median absolute deviation (NMAD), 68.3% quantile and 95% quantile. Additionally, root mean square errors before (RMSE) and after removing outliers (RMSE*) were calculated. The equations for all measures, as well as for the threshold for outliers can be found in Höhle and Höhle [10]. The statistical analyses were performed using the STATISTICA software (version 11) [32] and R programming language (version 3.3.3) [33].

To support statistical analyses, the visual assessment of UAV- and LiDAR-based DSMs, as well as the visual assessment of difference raster models was performed. Difference raster models were generated by subtracting LiDAR-based from UAV-based DSMs. Both, difference raster model generation and its visualization were conducted using Global Mapper (version 19) [34] and QGIS (version 2.18) [35] software.

RESULTS AND DISCUSSION

According to the described methods, DSMP (Figure 3a) and DSMP-GCP (Figure 3b) were generated. Detailed information on DSMs processing is presented in Table 3. It can be seen that computer processing time for both DSMs is almost the same, whereas the time spent on manual work is considerably greater for DSMP-GCP generation (30 min) than for DSMP generation (10 min). Namely, during the DSMP-GCP generation, most of the time (≈20 min) was spent on the manual detection of the GCPs on images, while for the DSMP generation the UAV images were orientated without using GCPs.

The results of the vertical agreement assessment of the UAV-based DSMs (DSMP and DSMP-GCP) with DSML conducted on 60 checkpoints of the regular 100 m sample grid are shown in Table 4. When comparing UAV-based DSMs with DSML, it is necessary to have in mind that between the acquisition of LiDAR and UAV data is a time gap of one year which corresponds with one vegetation and subsequently with annual height increment. According to the internal database (unpublished material) of Croatian Forest Research Institute, annual height increment for the forest of the study area ranges from 0.2 m to 0.45 m depending on tree species. As expected, DSMP-GCP shows higher accuracy, i.e. higher vertical agreement with DSML than DSMP. Namely, the horizontal accuracy (RMSEXY) of DSMP assessed with 7 GCPs (which were not used in its generation) is 5.67 m (Table 3). Since such horizontal errors may produce greater vertical errors [36], especially for surfaces with great variations in height on a small area (e.g. forest) [25, 37], the lower vertical agreement of DSMP with DSML is understandable. This is especially evident in Figure 3c, which shows a comparison of DSMs’ profiles through the exemplary area. By observing profiles at greater peaks, it can be seen that DSMP-GCP profile follows the DSML profile, whereas for DSMP profile the horizontal displacement of 5-10 m compared to DSML profile can be observed. The improvement in vertical agreement of UAV-based DSMs with DSML when GCPs are used can be observed visually on difference models (Figure 4). Similarly, when comparing two DSMs derived from WorldView-2 images, Hobi and Ginzler [38] found clear improvement of the DSM’s vertical accuracy when GCPs were used.

Furthermore, Figure 3c shows that DSML provides the highest discrimination of vertical forest structure clearly describing very steep variations in height (e.g. small gaps in the forest canopy, forest road). On the contrary, the profiles of both UAV-based DSMs are considerably smoother. Only bigger gaps in the forest canopy and a forest road (Figure 3a and 3b) can be detected, but in both cases, the vertical profiles of UAV-based DSMs do not reach the ground elevations. This is reasonable because LiDAR is an active sensor whose beams can penetrate through smaller gaps in the forest canopy and reach the ground, whereas the digital camera of UAV system used in this research (Table 1) is a passive optical sensor whose signal can characterize only the canopy surface [39].

Besides the technical limitations of low-cost UAV (e.g. camera quality, GNSS precision) used in this study and non-regular spatial distribution of GCPs, it can be suggested that the weather conditions (windy and cloudy weather) during UAV survey influenced image quality to a certain extent and consequently DSMs quality. The uncertainties are larger due to the complexity of the forest environment (e.g. moving trees, occlusions, shadows, images radiometric quality, etc.), which seriously affect the image matching procedure, and thus DSM quality [23, 25, 40, 41].

CONCLUSIONS

This research confirmed great potential of images obtained by low-cost UAV for forestry applications, even if they are surveyed in non-optimal weather (windy and cloudy weather) and environmental (inaccessibility for regular spatial distribution of GCPs) conditions. This could be of importance for cases when urgent UAV surveys are needed (e.g. detection and estimation of forest damage) which do not allow careful and longer survey planning. The vertical agreement assessment of UAV-based DSMs with LiDAR-based DSM confirmed the importance of GCPs for image orientation and DSM generation. Namely, a considerable improvement in vertical accuracy of UAV-based DSMs was observed when GCPs were used. While DSMs generated without GCPs can be used for visualisation and monitoring purposes, DSMs generated with GCPs have potential to be used in forest inventory. To confirm this, further research should focus on estimating the accuracy of tree and stand attributes.

Acknowledgments

This research has been supported by the Croatian Science Foundation under the projects IP-2016-06-7686 “Retrieval of Information from Different Optical 3D Remote Sensing Sources for Use in Forest Inventory (3D-FORINVENT)” and IP-2016-06-5621 “Geospatial Monitoring of Green Infrastructure by Means of Terrestrial, Airborne and Satellite Imagery (GEMINI)”. The authors wish to thank Hrvatske vode, Zagreb, Croatia, for providing ALS data.